As a Data Scientist at Monogram Health, you play a pivotal role in leveraging data to drive impactful healthcare solutions. This position is essential for developing models that enhance patient care and optimize operational efficiency. Your work directly influences the design and implementation of products that cater to patients' needs, ultimately leading to improved health outcomes.
The role of a Data Scientist at Monogram Health is not only about technical implementation but also involves strategic thinking and collaboration across various teams. You will engage with healthcare providers, product managers, and engineers to solve complex problems related to patient data analysis, predictive modeling, and improving service delivery. The scale and complexity of healthcare data make this position both challenging and rewarding, offering you the opportunity to make a meaningful difference in people's lives through data-driven insights.
Application Review
reportedWhoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.
What to demonstrate
- Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
- Whether your reason for wanting the role points at the work itself rather than the company's reputation
- Whether your language signals the level being screened for: what you decided yourself versus what you were handed
How to prepare
- Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
- Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
- Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands
Technical Interview
reportedBefore anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.
What to demonstrate
- Whether an ambiguous term becomes a specific column and filter before any computation happens
- Whether you read the schema for keys and cardinality rather than only for column names
- Whether the result answers the question at the grain it was asked at, per user or per session or per day
How to prepare
- Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
- On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
- Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over
Behavioral Interview
reportedRounds of this kind usually include one question about work that did not go well, and it is the part that carries the most information. Anyone can narrate a shipped win. What the interviewer learns from a project that stalled is how you behave without a result to hide behind: whether you noticed the problem yourself, how long it took, and who you told. Answers that route the failure onto a data pipeline or a reorganisation close the topic without answering it, and the follow-up comes back to your own part.
What to demonstrate
- Whether you found the error yourself or someone else found it, and how long it sat before anyone knew
- What you changed afterwards, stated as a check you now run rather than a lesson you now believe
- Whether the mistake you choose has real cost attached, such as a quarter of misdirected roadmap or a metric that was reported upward, instead of one that flatters you
How to prepare
- Choose a failure you caught yourself and be ready to say what tipped you off. A story where someone else caught it is still usable, but you will be asked why you missed it.
- Write down the check you added afterwards and where it lives now, so the correction is a concrete artefact rather than a resolution.
- Rehearse saying the cost out loud. Candidates shrink the number by instinct once the interviewer is in the room.
Final Interview
reportedA loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.
What to demonstrate
- Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
- Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
- Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience
How to prepare
- Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
- For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
- Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story
Offer Discussion
reportedRounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.
What to demonstrate
- Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
- The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
- Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options
How to prepare
- Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
- Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
- Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.
PracHub editorial advice for the preparation topics above.
Immortal time in adherence, treatment, and enrolment definitions
Classifying members as adherent, treated, or programme-enrolled requires them to survive and stay covered long enough to accumulate the defining events. That guaranteed event-free interval is assigned to the exposed group, so the exposure looks protective for reasons that have nothing to do with the treatment. Adherence studies are the classic case: measuring 12-month proportion of days covered and then comparing mortality builds survival into the exposure definition. Use time-varying exposure or a landmark analysis with the classification window excluded from follow-up.
Pre-post evaluation on a high-cost or high-risk cohort
Cohorts selected on an extreme value of the outcome regress toward the mean on their own. Members identified as the top 1 percent of spend in one year spend far less the next, whether or not anyone intervenes, because the selecting year captured both chronic severity and one-off events. A pre-post design on such a cohort will report savings every time. A concurrent comparison group selected by the same rule in the same period, or a regression discontinuity at the selection threshold, is the minimum credible design.
Reporting a mean for a heavy-tailed metric without saying what it hides
For spend, session length or items per order, a small fraction of units carries most of the total, so the mean has a wide standard error and one account can move it. Fix the handling before you see the result: cap or winsorise at a pre-declared percentile, and report the median or the share above a threshold next to the mean. Capping changes the estimand, so say which question the capped number answers, and check how much of any difference comes from the top 0.1 percent of units.
Answering a product-sense question with a list of features
Answer with a decision and the measurement that would settle it: the hypothesis, the primary metric, the guardrails, and the result that would make you not ship. A feature brainstorm cannot be wrong, which is exactly why it earns no points.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you differentiate between correlation and causation in your ana…
How do you differentiate between correlation and causation in your analyses?
Approach
- Say what the estimate is of, and over what population it generalises.
- Translate the result into the decision it informs, in one plain sentence.
- Write down the assumption the method needs before you use the method.
Follow-up
- How would you explain this result to someone who does not know statistics?
- What sample size would you need to detect an effect half this size?
Write a function to calculate the mean and standard deviation of a dat…
Write a function to calculate the mean and standard deviation of a dataset.
Approach
- Sanity-check the answer against a simple bound or a simulated case.
- Say what the estimate is of, and over what population it generalises.
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
Follow-up
- How would you explain this result to someone who does not know statistics?
- What sample size would you need to detect an effect half this size?
Describe a machine learning algorithm you have implemented and the res…
Describe a machine learning algorithm you have implemented and the results it produced.
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Check what information would not exist at prediction time, and exclude it.
- Set a baseline first, so any model has something honest to beat.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- Where could label leakage enter this setup?
Expand overlapping coverage spans into fractional member-months
member_enrollment has span_id, member_id, plan_id, product_type, effective_date, termination_date (NULL while active). Both dates are inclusive. Write a function returning one row per calendar month of a stated year with total member-months, where a member contributes covered days in the month divided by days in that month, capped at one month per member even when two spans overlap after a plan change or a retroactive span. Do not expand to one row per member-day: the sample has 400,000 members. Output: month, member_months.
Approach
- Resolve NULL termination_date to the reporting end date and say so in a comment. An open span is not an infinite span, and clipping it at the year end is what keeps the denominator finite and auditable.
- Merge overlapping and adjacent spans per member before touching months. Sort by member_id and effective_date, carry a running maximum of the end date, and open a new merged group when effective_date exceeds that running max plus one day. Adjacent means a gap of zero days, which a plan change produces constantly.
- Cross join the merged spans to the 12 month boundaries rather than to days. Overlap days = (min(span_end, month_end) - max(span_start, month_start)).days + 1, clipped below at 0. That is at most 12 rows per span instead of 365.
- Divide overlap days by the number of days in that month, so February and July are weighted correctly, then sum by month.
- Validate on a constructed member before trusting the aggregate: a single span covering the whole year must sum to exactly 12.0.
Worked solution 30 min
- Fill termination_date nulls with the reporting end date and clip all spans to the reporting year.
- Sort by member_id, effective_date, then compute a running max end per member with cummax shifted by one, flag a new group where effective_date > prior_max_end + 1 day, and cumsum the flag to get merged group ids.
- Aggregate each group to min start and max end, producing disjoint spans per member.
- Cross join merged spans to a 12-row month frame, compute clipped overlap days, divide by days in month.
- Group by month and sum.
Follow-up
- Product type changes mid-year. The metric must be reported by product_type. Where does the cap now apply and what breaks?
- An eligibility file arrives with a retroactive termination that shortens a span you already reported on. How do you restate?
- Why member-months rather than distinct members, in one sentence, for a director who wants the simpler number?
Discuss any experience you have with data visualization libraries.
Discuss any experience you have with data visualization libraries.
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- State the window function and its partition and ordering out loud before writing it.
- Say which table is the grain you start from, and join outward from it.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
What programming languages do you prefer for data analysis, and why?
What programming languages do you prefer for data analysis, and why?
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- State the window function and its partition and ordering out loud before writing it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
Follow-up
- How would you verify this result without re-running the same query?
- What breaks if events arrive late or out of order?
How would you optimize a SQL query that is running slowly?
How would you optimize a SQL query that is running slowly?
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
Deduplicate claim versions before totalling allowed amounts
medical_claim_line holds one row per service line per claim version: claim_line_id, claim_id, claim_version, frequency_code (1 original, 7 replacement, 8 void), member_id, service_start_date, procedure_code, allowed_amount, claim_status and adjudicated_at. Adjusted claims appear more than once, so summing every row double counts them. Return total allowed_amount by procedure_code for service_start_date in Q1 2025, counting only lines with claim_status 'paid' on the surviving version of each claim, which is the highest claim_version per claim_id, and dropping the claim entirely when that surviving version carries frequency_code 8. Resolve survivorship with a window function, not a self-join.
Approach
- Resolve survivorship at claim_id, not claim_line_id. Carry MAX(claim_version) OVER (PARTITION BY claim_id) alongside every line and keep lines where claim_version equals it, because a replacement version can contain a different number of lines than the original.
- Read the frequency_code of the surviving version only. A frequency_code 8 removes the claim outright; it does not revert payment to the prior version, so a void must delete the claim rather than promote version n-1.
- Apply claim_status and the date filter after survivorship is resolved. Filtering paid lines first can strip the surviving version and silently elect a superseded one.
- Group by procedure_code and sum allowed_amount, then state the paid-through date on the output because Q1 amounts keep moving until adjudication runout completes.
- Sanity check the shrinkage: report how many claim_ids and how many dollars the dedup removed, so the reviewer can see the step did something.
Worked solution 20 min
- Build a CTE that adds surviving_version = MAX(claim_version) OVER (PARTITION BY claim_id) and surviving_freq = the frequency_code of the row at that version, to every line.
- Filter to claim_version = surviving_version so all lines of the surviving version survive together.
- Drop claims whose surviving_freq is 8, then apply claim_status = 'paid' and service_start_date between 2025-01-01 and 2025-03-31.
- Group by procedure_code, sum allowed_amount, and emit the count of distinct claim_ids contributing.
- Run the naive SUM over all rows beside it and report the difference as the double count that was removed.
Follow-up
- Two rows share a claim_id and claim_version but differ on adjudicated_at. What do you do, and what does that imply about the extract?
- How does the query change if the request is allowed_amount by paid_date rather than service_start_date, and which basis does a finance reconciliation want?
- A void arrives for a claim already published in a closed month. How do you restate without rewriting history in the warehouse?
How do you prioritize tasks when you have multiple deadlines?
How do you prioritize tasks when you have multiple deadlines?
Approach
- Fix the population and the time window before naming any metric.
- Restate the decision this analysis has to support, and who acts on the answer.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
If you were tasked with reducing patient wait times using data, what s…
If you were tasked with reducing patient wait times using data, what steps would you take?
Approach
- Fix the population and the time window before naming any metric.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
Explain how you would design an experiment to test a new healthcare in…
Explain how you would design an experiment to test a new healthcare intervention.
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- State the primary metric and the minimum effect worth shipping, then size the test.
- Name the randomisation unit first; it decides the variance and what the test can detect.
Follow-up
- How would you handle interference between treated and control units?
- What would you do if you could not randomise at all?
What methods do you use for feature selection?
What methods do you use for feature selection?
Approach
- Say what you would check first and why it is the highest-information step.
- Clarify what is being asked and what a complete answer would contain.
- Work from the decision backwards to the evidence you would need.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Redesign an adherence metric an outreach team cannot quietly inflate
An outreach team is paid on the share of members reaching proportion of days covered of 0.80 or higher in a therapeutic class, computed from pharmacy_claim (member_id, therapeutic_class_code, fill_date, days_supply, refill_number, reversal_flag, reversed_fill_id). The denominator is members with at least two fills in the class and at least 91 days of follow-up, over a 12-month window. Name three ways the team raises this number without any member taking more medication, then redesign the metric and its guardrails so each lever is closed or visible.
Approach
- Attack the denominator first, because it is the cheapest lever. A member who fills once and abandons never enters the denominator at all, so the worst outcome in the class is invisible, and any effort spent on first-fill abandonment lowers the reported share by moving those members in. The incentive runs backwards.
- Attack the window arithmetic second. With the denominator running from first fill to window end, a member whose first fill lands late in the year has a short denominator that is trivially easy to cover, so steering new starts to the last quarter raises the metric mechanically. Report by index quarter or fix the window and require the index fill in the first quarter.
- Attack the threshold third. Days of supply dispensed late in the window count only up to the window end, but a fill placed 60 days before close, on a member whose last on-hand day has already passed, adds up to 60 covered days, about 0.16 of a 365-day denominator. That alone moves a member from 0.77 to 0.93 with no change in what anyone swallows.
- Close the dispensing-side hole: a fill that is billed and reversed is not exposure. Exclude both rows of a reversed pair via reversal_flag and reversed_fill_id, and collapse overlapping fills by pushing each interval start to the later of fill_date and the prior interval's end, so early refills shift coverage forward rather than stacking.
- Redesign the scorecard as a pair rather than a single number: the threshold share stays as the headline, and mean PDC plus the share at 0.90 or higher travel with it, because a distribution cannot be bunched at a threshold without showing it.
- Add the guardrails that make each lever visible: first-fill abandonment rate (fills with refill_number 0 never followed by a second fill, plus reversed first fills), the share of covered days originating from fills in the final 60 days of the window, and the distribution of PDC in the bands just below and just above 0.80.
Worked solution 30 min
- Write the exposure-interval construction explicitly: drop reversed pairs, sort fills per member and class by fill_date, set each interval start to the greater of fill_date and the prior interval end, and truncate the final interval at the window end.
- Write the denominator in two variants, the incumbent at-least-two-fills rule and an index-fill rule requiring the first fill in the first quarter with at least 273 days of follow-up, and state what each includes that the other does not.
- Enumerate the three gaming levers with the arithmetic that makes each one work, including the 60-day end-of-window fill computed against a 365-day denominator.
- Specify the headline metric plus the two companions (mean PDC, share at 0.90 or higher) and the three guardrails, each with numerator, denominator and window.
- Write one sentence stating that dispensing is a proxy for taking and that the proxy overstates adherence in the direction of anyone who fills and does not take.
Follow-up
- The share at 0.80 or higher rose four points and mean PDC rose 0.4 points. What is your reading, and what do you look at next?
- Would you pay on 0.80 at all, or on the continuous measure? Say what each choice costs you.
- Possession is not ingestion. Name a measurable consequence in another table that would corroborate a real adherence improvement, and state its own weakness.
First-pass denial rate doubled in one received week
First-pass denial rate jumped from 6.1 to 11.8 percent in a single received_date week and stayed at the new level. Nothing in the adjudication rules changed that week. You have medical_claim_line: claim_id, claim_version, frequency_code, claim_status, denial_reason_code, member_id, received_date, adjudicated_at, billing_provider_npi. The metric counts lines whose first adjudication returns 'denied', over lines received in the window at claim_version = 1. Find the cause, say whether denial behaviour actually changed, and deliver the corrected series plus the query change that fixes it.
Approach
- Cut the excess by denial_reason_code before anything else. A behaviour change spreads across codes; an upstream failure concentrates in one. If a single eligibility-related code carries nearly all of the 5.7 point excess, the claims are fine and the eligibility data was not.
- Join the denied lines to member_enrollment and compare the span's created_at to the claim's received_date. Lines denied for members whose coverage span loaded after the claim arrived are a load-ordering failure, not a coding failure.
- Check resolution downstream: take those claim_ids and look for a later version with frequency_code 7 that adjudicates to 'paid'. A high resubmit-and-pay share confirms the denial was transient and recoverable.
- Verify the denominator is not the mover. Count lines at claim_version = 1 by received_date and check the weekday mix, because submission batching makes received_date strongly day-of-week patterned and a short or holiday week shifts the denominator without changing behaviour.
- Confirm runout: the metric should be held until 60 days of adjudication have passed, so check that the comparison weeks are equally mature rather than comparing a settled week to a fresh one.
- Restate the series with the affected reason code broken out as its own line, and change the query to carry denial_reason_code into the reported breakdown rather than only the headline rate.
Follow-up
- The affected lines all get paid on resubmission. Is the first-pass denial rate then wrong, or right and uninteresting?
- How would you monitor for this class of failure without waiting for someone to notice a chart?
- What changes if the excess is spread evenly across denial_reason_code instead of concentrated?
For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Design one test end to end on paper
- Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
- Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
- State in advance what you will do if the primary metric is flat while a secondary metric is significant.
Deliverable: A one-page test design with a decision rule written before launch.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Power arithmetic until it is automatic
- Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
- Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
- Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.
Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Variance and the unit-of-analysis problem
- Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
- Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
- Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.
Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04Validity threats you can actually test for
- Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
- Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
- Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.
Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.
Practice prompt ↗Practice prompt ↗Worked solution ↗05When randomization is not available
- Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
- Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
- List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.
Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.
Practice prompt ↗Practice prompt ↗06The readout query
- Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
- Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
- Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.
Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.
Practice prompt ↗Practice prompt ↗07Present it to someone who will not read the appendix
- Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
- Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
- Rewrite your opening line so the recommendation lands before any methodology.
Deliverable: A one-page readout whose first line is the recommendation.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Half of this section is about translation. Be ready to describe how you explained a result to someone who did not want the method, only the implication, and what you did when the simplified version started being repeated in a way that overstated it. Correcting your own simplification is a strong beat.
What motivates you to work in the healthcare sector?
What motivates you to work in the healthcare sector?
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- Pick a story where you drove the decision, not one where you observed it.
- Close with what you would do differently, concretely.
Follow-up
- What did you decide not to do, and why?
- How did you know the outcome was caused by your change?
Sequence three urgent requests with one analyst-week available
Three requests land on Monday and you have one week. An actuarial team needs incurred-claims completion factors restated before a filing deadline on Thursday. A clinical programme owner wants a deterioration model refreshed because its calibration has drifted in one region. A trial operations team wants site enrolment forecasts for a portfolio review in two weeks. Each requester believes theirs is blocking. Deliverable: your sequence with the reasoning, the message you send to whoever is deprioritised, and the smaller artefact you hand each of the two you cannot fully serve.
Approach
- The probe is whether you prioritise on consequence and reversibility rather than on who asked loudest or most recently.
- Classify each request by what happens if it slips. A regulatory or contractual deadline is irreversible on its date, a drifting model is causing harm every day it keeps running, and a portfolio review can absorb a provisional number. That ordering is defensible to all three requesters because it does not depend on your preferences.
- Take the deadline-bound work first, but scope it to the minimum defensible output, because completion factors feeding a filing carry a different error tolerance than a slide.
- Do not let the drifting model simply wait. Quantify the harm cheaply by comparing calibration in the affected region against the rest, and if it is materially miscalibrated propose flagging or suppressing its output for that region within the hour rather than at the end of a refresh.
- Give each deprioritised requester something real: a provisional forecast with its uncertainty and a refresh date, or a diagnostic that tells them whether their problem is urgent. Say no explicitly with a date rather than going quiet, because silence is what produces escalation.
Follow-up
- The programme owner escalates to your manager. What do you want your manager to be able to say?
- Midweek the actuarial work needs two more full days than you estimated. What gives?
- How does your answer change if the drifting model drives a clinical outreach list rather than a report?
Scope a one-line request for a readmission rate
A clinical operations lead messages you: 'What is our readmission rate? The committee meets Friday.' You have encounter (encounter_id, admit_ts, discharge_ts, encounter_type, admission_type, discharge_disposition, drg_code, index_encounter_id) and member_enrollment coverage spans. Do not open a query editor yet. Deliverable: the three to five questions you send back, ranked by how much the answer moves the number; the default specification you will build if nobody replies by Wednesday; and the one line you will place under the figure so the committee does not read it against a published benchmark.
Approach
- Read the probe: this tests whether you convert ambiguity into a specification without either stalling or guessing silently. Both failure modes are common, and the silent guess is worse because nobody can see it.
- Rank your questions by leverage on the number rather than by curiosity. Whether the numerator is all-cause or unplanned, and whether the clock starts at discharge_ts, move the result far more than a tie-break rule on same-day returns.
- Ask about the comparison first. A number going to a committee will be compared to something, and whether that something is last quarter, another panel, or a national figure decides whether you owe them a raw rate, a risk-adjusted rate, or an observed-over-expected ratio.
- Commit to a default in writing so silence does not block you: unplanned acute inpatient readmission within 30 days of discharge_ts, index stays excluding planned admissions, acute-to-acute transfers, discharges against medical advice, and dispositions of expired or hospice, denominator of eligible index discharges, paid-through date stated.
- Write the caveat as a restriction on use rather than a hedge: name the one comparison the figure supports and the one it does not.
Follow-up
- They reply that they want it 'the way the benchmark does it'. What do you ask next?
- The committee wants it split by attending provider. What changes in the specification, and what do you refuse to show?
- How does your answer change if the meeting is tomorrow rather than Friday?
- 01
What motivates you to work in the healthcare sector?
- 02
Three requests land on Monday and you have one week. An actuarial team needs incurred-claims completion factors restated before a filing deadline on Thursday. A clinical programme owner wants a deterioration model refreshed because its calibration has drifted in one region. A trial operations team wants site enrolment forecasts for a portfolio review in two weeks. Each requester believes theirs is blocking. Deliverable: your sequence with the reasoning, the message you send to whoever is deprioritised, and the smaller artefact you hand each of the two you cannot fully serve.
- 03
A clinical operations lead messages you: 'What is our readmission rate? The committee meets Friday.' You have encounter (encounter_id, admit_ts, discharge_ts, encounter_type, admission_type, discharge_disposition, drg_code, index_encounter_id) and member_enrollment coverage spans. Do not open a query editor yet. Deliverable: the three to five questions you send back, ranked by how much the answer moves the number; the default specification you will build if nobody replies by Wednesday; and the one line you will place under the figure so the committee does not read it against a published benchmark.
Is this an official Monogram Health interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Monogram Health. Rounds and questions reflect what candidates have reported, not a process Monogram Health has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the interviews and how much preparation time is typical?
Interviews at Monogram Health can be challenging, requiring thorough preparation in both technical and behavioral aspects. Candidates typically spend several weeks preparing, focusing on relevant projects and concepts.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates demonstrate not only technical proficiency but also strong communication skills and a genuine passion for improving healthcare outcomes through data.
PracHub interview research ↗What is the company culture like at Monogram Health?
Monogram Health fosters a collaborative and innovative environment, emphasizing teamwork and a commitment to patient-centered solutions.
PracHub interview research ↗What is the typical timeline from initial screen to offer?
The timeline can vary, but candidates usually receive feedback within 2-4 weeks after their initial interview.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
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